Wie klassifiziere ich das Bild in Echtzeit mit Tensorflow?

Question

Ich versuche Himbeer Pi-Kamera zu verwenden, um Bild zu erfassen und das Bild in Echtzeit in drei Klassen zu klassifizieren. Was ich getan habe, ist der folgende Code. Es kann in der ersten Iteration vorhersagen. Das Problem ist, dass es mir zeigt, dass mir nach der zweiten Iteration der Speicher ausging. Gibt es trotzdem etwas zu beheben?

import numpy as np 
import tensorflow as tf 
import argparse 
import os 
import sys 

def create_graph(model_file): 
    """Creates a graph from saved GraphDef file and returns a saver.""" 
    # Creates graph from saved graph_def.pb. 
    with tf.gfile.FastGFile(model_file, 'rb') as f: 
     graph_def = tf.GraphDef() 
     graph_def.ParseFromString(f.read()) 
     _ = tf.import_graph_def(graph_def, name='') 


def run_inference(images, out_file, labels, model_file, k=5): 

    # Creates graph from saved GraphDef. 
    create_graph(model_file) 

    if out_file: 
     out_file = open(out_file, 'wb', 1) 

    with tf.Session() as sess: 
     softmax_tensor = sess.graph.get_tensor_by_name('final_result:0') 
     for img in images: 
      if not tf.gfile.Exists(img): 
       tf.logging.fatal('File does not exist %s', img) 
       continue 
      image_data = tf.gfile.FastGFile(img, 'rb').read() 


      predictions = sess.run(softmax_tensor, 
           {'DecodeJpeg/contents:0': image_data}) 
      predictions = np.squeeze(predictions) 
      top_k = predictions.argsort()[-k:][::-1] # Getting top k predictions 

      vals = [] 
      for node_id in top_k: 
       human_string = labels[node_id] 
       score = predictions[node_id] 
       vals.append('%s=%.5f' % (human_string, score)) 
      rec = "%s\t %s" % (img, ", ".join(vals)) 
      if out_file: 
       out_file.write(rec) 
       out_file.write("\n") 
      else: 
       print(rec)  
    if out_file: 
     print("Output stored to a file") 
     out_file.close() 

if __name__ == '__main__': 
    parser = argparse.ArgumentParser(description='Classify Image(s)') 
    parser.add_argument('-i','--in', help='Input Image file ') 
    parser.add_argument('-li','--list', help='List File having input image paths') 
    parser.add_argument('-o','--out', help='Output file for storing the content') 
    parser.add_argument('-m','--model', help='model file path (protobuf)', required=True) 
    parser.add_argument('-l','--labels', help='labels text file', required=True) 
    parser.add_argument('-r','--root', help='path to root directory of input data') 
    args = vars(parser.parse_args()) 
    # Read input 
    if not args['in'] and not args['list']: 
     print("Either -in or -list option is required.") 
     sys.exit(1) 
    if args['in']: 
     images = [args['in']] 
    else: # list must be given 
     with open(args['list']) as ff: 
      images = filter(lambda x: x, map(lambda y: y.strip(), ff.readlines())) 

    # if a separate root directory given then make a new path 
    if args['root']: 
     print("Input data from : %s" % args['root']) 
     images = map(lambda p: os.path.join(args['root'], p), images) 

    with open(args['labels'], 'rb') as f: 
     labels = [str(w).replace("\n", "") for w in f.readlines()] 

    while True: 
     imagename='/home/pi/Desktop/camerasnap.jpg' 
     images=raspi.capture(imagename) 
     run_inference(images=images, out_file=args['out'], labels=labels, model_file=args['model']) 

Answer 1

Das Problem ist, dass Sie die Grafik in jedem run_inference Methodenaufruf erstellen:

while True: 
     imagename='/home/pi/Desktop/camerasnap.jpg' 
     images=raspi.capture(imagename) 
     run_inference(images=images, out_file=args['out'], labels=labels, model_file=args['model']) 


def run_inference(images, out_file, labels, model_file, k=5): 

    # Creates graph from saved GraphDef. 
    create_graph(model_file) 
    ... 

Wie die Grafik wahrscheinlich fast alle Speicher in Ihrem GPU verwendet versagt es in der zweiten Iteration, wenn eine der Code versucht, ein neues Diagramm zu erstellen. Sie sollten nur ein Diagramm für die gesamte Programmlebensdauer erstellen.

Try this:

create_graph(model_file) 
while True: 
     imagename='/home/pi/Desktop/camerasnap.jpg' 
     images=raspi.capture(imagename) 
     run_inference(images=images, out_file=args['out'], labels=labels, model_file=args['model'])